MLOps Engineer

Architect and maintain the ML infrastructure powering a global fintech platform that processes over $50B in transactions annually, where system reliability and performance are absolutely critical. You'll build sophisticated MLOps pipelines using Kubernetes, MLflow, and custom tooling to deploy and monitor hundreds of ML models in production. The project involves implementing advanced monitoring systems for model drift detection, building automated retraining pipelines, and ensuring 99.99% uptime for mission-critical AI services. You'll work with cutting-edge technologies including feature stores, model registries, and distributed training systems while collaborating closely with data scientists to streamline the model development lifecycle. This role requires deep technical expertise in both machine learning and infrastructure engineering, offering the opportunity to solve complex scalability challenges while working with some of the most advanced ML systems in the industry. Your work will directly enable data scientists to deploy models faster and more reliably, making you a key enabler of the company's AI capabilities.

• Design and implement ML deployment pipelines
• Monitor model performance and data drift
• Manage ML infrastructure and scaling
• Implement automated testing for ML systems
• Collaborate with data scientists on model deployment

• 3+ years of DevOps or MLOps experience
• Strong knowledge of containerization and orchestration
• Experience with ML model deployment and monitoring
• Proficiency in cloud platforms and infrastructure as code
• Understanding of CI/CD best practices